SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 1621–1630 of 15113 papers

TitleStatusHype
Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments—0
Reward-Directed Score-Based Diffusion Models via q-Learning—0
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions—0
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy ChurnCode0
Reward Guidance for Reinforcement Learning Tasks Based on Large Language Models: The LMGT Framework—0
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization—0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
CHIRPs: Change-Induced Regret Proxy metrics for Lifelong Reinforcement Learning—0
Differentiable Discrete Event Simulation for Queuing Network Control—0
ELO-Rated Sequence Rewards: Advancing Reinforcement Learning ModelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified